Method and apparatus for ranking-based information processing

ABSTRACT

Techniques are provided for ranking-based information processing. Method steps can include integrating information from at least one source (a plurality of heterogeneous sources can also be handled), to obtain integrated information; mapping the integrated information to at least one application; ranking the integrated information based on the mapping and on ranking criteria from a knowledge base; and processing the integrated information, based on the ranking, to obtain processed integrated information. Optionally, the processing step includes formatting the processed integrated information for a plurality of applications.

FIELD OF THE INVENTION

The present invention generally relates to information technology, and,more particularly, to a method and apparatus for information processing.

BACKGROUND OF THE INVENTION

It is estimated that large enterprises are witnessing tremendous yearlygrowth in information handling (more than 100%), based on the productsand services that they offer. Processing significant parts of theinformation that is generated is important, so as to facilitate soundand timely decision making with regard to, e.g., policies and processes.Enterprise information systems usually connect to various sources acrossmultiple internal and external third-party application systems.

If enterprises wish to adapt in real-time, they may need to monitor,integrate, analyze, and respond to high volume information from diverseapplications and information sources. The amount of information to beconsidered can be hundreds of terabytes, depending on the granularitylevels. One may encounter unstructured information from raw events on aparticular enterprise metric from one of more sources, as well asaggregated information such as parts sales, ecommerce transactions,customer complaints, warranty claims, and the like.

Techniques such as filtering, integration, and summarization have beenused for managing, searching and processing high volume enterpriseinformation. Filtering is discussed in Y. Zhang, “Using Bayesian Priorsto Combine Classifiers for Adaptive Filtering,” in the Proceedings ofSIGIR 2004. Integration is discussed in, e.g., Y. Arens et al., “Queryreformulation for dynamic information integration,” in the Journal ofIntelligent Information Systems, 1996. Summarization is discussed in,e.g., M. Hu and B. Liu, “Mining and Summarizing Customer Reviews,” inthe Proc. of ACM SIG KDD, 2004. These are powerful techniques formanaging high volume enterprise information. Nevertheless, the continuedgrowth in the volume and complexity of information being handledrequires continued progress.

It would thus be desirable to overcome the limitations in previousapproaches.

SUMMARY OF THE INVENTION

Principles of the present invention provide techniques for ranking-basedinformation processing. An exemplary method (which can becomputer-implemented) for ranking-based information processing,according to one aspect of the invention, can include steps ofintegrating information from at least one source (a plurality ofheterogeneous sources can also be handled), to obtain integratedinformation; mapping the integrated information to at least oneapplication; ranking the integrated information based on the mapping andon ranking criteria from a knowledge base; and processing the integratedinformation, based on the ranking, to obtain processed integratedinformation.

One or more embodiments of the invention can be implemented in the formof a computer product including a computer usable medium with computerusable program code for performing the method steps indicated.Furthermore, one or more embodiments of the invention can be implementedin the form of an apparatus including a memory and at least oneprocessor that is coupled to the memory and operative to performexemplary method steps.

One or more embodiments of the invention may provide one or morebeneficial technical effects, such as, for example, screening, rankingand scoring the most important information for users and/or systems toact on or process.

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram depicting an exemplary embodiment of anapparatus for ranking-based information processing, according to anaspect of the present invention, which is also indicative of anexemplary data flow corresponding to exemplary method steps forranking-based information processing;

FIG. 2 is one specific exemplary instantiation of an apparatus andmethod as depicted in FIG. 1;

FIG. 3 shows exemplary importance factors for a typical vehicle part,one of many possible applications of one or more inventive techniques;

FIG. 4 is a flow chart of exemplary method steps that can beimplemented, e.g., by a ranking-based display tool; and

FIG. 5 depicts a computer system that may be useful in implementing oneor more aspects and/or elements of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 1 shows a system block diagram 100 depicting an exemplaryembodiment of an apparatus for ranking-based information processing,according to an aspect of the present invention, which is alsoindicative of an exemplary data flow corresponding to exemplary methodsteps (which can be computer-implemented) for ranking-based informationprocessing. The system 100 accepts information 102 that includes bothinformation entity 104, to be consumed by applications, and that to beused for extracting process knowledge 106. The output of the system 100can be, e.g., entity priority information which enriches knowledge base108. The ranking information can benefit, for example, applications orhuman analysts performing intensive analysis on a large body ofenterprise process information.

The exemplary system 100 contains two processes 110, 112. The latterbuilds up ranking criteria by integrating process knowledge, as shown atknowledge extraction and integration blocks 114, 116 and rankingcriteria generation block 118. The former weighs information entitieswith ranks by applying ranking criteria, which can be, e.g., built-in.This is depicted at preprocessing block 120, integration block 122,classification block 124, filtering block 126, and ranking by criteriablock 128.

By way of example and not limitation, several scenarios mayinstructively be considered. Enterprises may wish to know from partssales (happening in the hundreds of thousands a month) what parts arebeing purchased, by whom, which models, and why. Further, enterprisesmay wish to know, from the aftermarket in the automotive industry, thefailures, symptoms, parts that were replaced, and actions taken to fixvehicles. This information may spread across multiple applicationsincluding the Web. Therefore, processing and extracting knowledge fromhigh-volume enterprise information entities and events in real-time is amajor challenge for responsive enterprise systems.

One or more techniques disclosed herein may advantageously provide aranking mechanism rooted in reasoning enterprise knowledge from varioussources. Three major challenges may arise in this scenario due to theheterogeneity in information and environment. In the following wedescribe the three potential problems.

The first problem is source heterogeneity. Enterprise information isgenerated by different sources, including commercial databases inmulti-parties, web sites, enterprise processes that can be wrapped withweb services, and the like. These sources may be distinct not only intheir formats but also in their natures. For example, in automotiveenterprises, commercial databases provide manufacture information andaftermarket information such as warranty claims, parts availability, andprices, while some websites collect customer reviews and complaints.Consequently, source heterogeneity brings challenges in informationintegration and both knowledge extraction and integration.

The second potential problem is due to knowledge heterogeneity. Theknowledge about the importance rank of information can be learned fromvarious sources. For example, analysis on history information mayprovide predictions pertaining to the importance of future events;semantic knowledge about relations between information entities andobjects in analysis is another exemplary type of knowledge. Integratingmulti-dimensional enterprise knowledge and building uniform rankingcriteria is the second exemplary challenge regarding heterogeneity.

Finally, a ranking mechanism should be adaptive to a given enterpriseenvironment, this issue is referred to herein as the problem of contextheterogeneity. The importance of enterprise information depends on adynamic environment with variables and parameters such as time,location, user's activity, etc.

The three types of heterogeneity discussed above may be advantageouslyhandled in the two processes 110, 112.

By way of example and not limitation, knowledge heterogeneity isdiscussed herein in connection with the importance of combiningmulti-dimensional enterprise knowledge. Integrating enterprise knowledgefrom various sources may be particularly useful. However, no limitationin scope is intended, and approaches to deal with other two challengesare also discussed.

One or more techniques disclosed herein may provide a general filteringframework and a family of ranking mechanisms for extracting crucialknowledge from high-volume enterprise information management.

Handling Source Heterogeneity and Context Heterogeneity

The ranking process 110, can handle source heterogeneity, i.e., thedifferences in information sources. Source heterogeneity can be dealtwith using two models, preprocessor 120 and integrator 122.Source-specific preprocessing 120 prepares data using techniques such asformat transformation. Information integration 122 handles thedifferences in the interface of data sources. Semantic-based informationintegration can facilitate the integration from heterogeneous sources.Given the teachings herein, one can adapt well-known techniques such asthose disclosed in M. Chen et al., “Semantic Query Transformation forIntegrating Web Information Sources,” in Proc. of ICEIS 2005.

Classification as per block 124 associates enterprise information to theapplications that consume the information. By differentiating enterpriseinformation by interesting parties, one can apply filtering rules basedon the requirement profiles of the applications.

Context heterogeneity can be dealt with in either ranking process orranking criteria generation. One approach takes static enterpriseknowledge and generates multi-dimensional ranking criteria that alignwith context profiles. Therefore, in applying ranking criteria, anapplication can extract ranking results according to its contextprofile. This approach is referred to herein as static context-basedranking. Alternatively, context information can become a knowledgesource to form ranking criteria on the fly. This approach is referred toherein as run-time context-based ranking.

Ranking Criteria Generation Considering Knowledge Heterogeneity

Enterprise knowledge that is used to build ranking criteria can beextracted from multiple sources, such as ontology of information, todescribe relations between components, analysis results on historyinformation, etc. To deal with knowledge heterogeneity, one exemplaryranking criteria generation uses a two-phase model.

In phase 1, knowledge is learned from each knowledge source usingindependent learning algorithms. For example, various mining techniquescan be applied to learn enterprise trends from history information andto predict enterprise behaviors in the future. The correlation of aninformation entity to the next event as predicted can be used todetermine the importance of the information entity. Besides theanalytics results, semantic knowledge that describes the relationbetween information objects provides another knowledge source forranking. Consider an exemplary case wherein automotive analysts set theengine as a critical system to monitor. Then, the information about thecomponents that have dramatic influences on engine performance shouldalso carry high weights. There are many other sources for extractingknowledge with regard to information importance.

With ranking knowledge learned from multiple sources using independentlearning algorithms, phase 2 integrates the ranking knowledge togetherand forms unified ranking criteria. One possible inventive integrationtechnique takes a weighted average of ranking results along variousknowledge dimensions as shown in the following formula:

$\begin{matrix}{S = \frac{\sum\limits_{i}\left( {W_{i} \cdot V_{i}} \right)}{\sum\limits_{i}W_{i}}} & (1)\end{matrix}$

-   -   Where    -   V_(i): value of dimension i    -   W_(i): weight of dimension i

In the above formula, the weighting function is significant forbalancing the effects of different and independent types of knowledge.Several mechanisms can be used to determine the weights. Manualweighting refers to the method that uses pre-defined weights given bydomain experts. Confidence-based weighting is an approach that weighs aranking result according to the confidence in the result, which may beespecially suitable when the resulting knowledge is generated based onprediction or from limited samples. Hybrid weighting combines bothmanual weighting and confidence-based weighting by allowing users setweights for some knowledge dimensions while automatically giving weightsto the others.

Using any weighting approach, the ranking function should be normalizedboth within each knowledge dimension and across dimensions.Normalization within each knowledge dimension is performed to keepranking results independent from one dimension to another. Normalizationacross dimensions controls the domain of the final ranking results.

As noted, FIG. 1 is not only an exemplary system block diagram, but alsoan exemplary flow chart. It will be appreciated that a method forranking-based information processing can include the steps ofintegrating information from at least one source, or indeed from aplurality of heterogeneous sources, as at block 122, to obtainintegrated information; mapping the integrated information to at leastone application, for example, using a classifier 124; ranking theintegrated information based on the mapping and on ranking criteria froma knowledge base 108, as at block 128 and processing the integratedinformation, based on the ranking, to obtain processed integratedinformation (for example, via one or more applications interfacing withthe entity rank of knowledge base 108 (which also can hold the rankingcriteria)). Thus, if desired, the integrated information can be mappedto a plurality of applications 130, and the processing step can includeformatting the processed integrated information for the plurality ofapplications. The knowledge base 108 can include the aforementionedranking criteria store 132 and entity rank store 134. The rankingcriteria can be stored in the ranking criteria store, and the results ofthe ranking of the integrated information can be stored in the entityrank store.

Additional steps can include obtaining the information from theplurality of heterogeneous sources (e.g., information entities 104), andpreprocessing the information, as at block 120, to facilitate theintegrating step. Further, additional steps can include extractingprocess knowledge, as at block 114, from a plurality of processknowledge sources 106, and obtaining ranking criteria for the knowledgebase, such as via ranking criteria generation 118, based at least on theprocess knowledge.

A further additional step of normalizing the process knowledge to obtainnormalized process knowledge can be conducted (for example, as part ofknowledge integration block 116), wherein the process knowledge on whichthe ranking criteria are based is the normalized process knowledge.

The ranking can be based at least on, e.g., a weighted average along aplurality of knowledge dimensions, a polynomial ranking function, and/oran exponential ranking function.

EXAMPLE

Ranking Automotive Parts for Failure Analysis

A ranking-based information filtering framework for aftermarket analysison vehicle failures will be demonstrated to illustrate one of manypotential applications for one or more inventive techniques set forthherein. In a world-class automotive company, quality analysts may face alarge body of information about auto failures. The information to beanalyzed may comes from, e.g. warranty claims, call centers, suppliers,partners, customer reviews, sensor systems, etc. Information filteringcan facilitate information processing in such an environment.

FIG. 2 depicts a conceptual information processing system 200 thatfilters enterprise information based on a ranking mechanism. Integrationof a variety of information is performed by information integrationblock 202, with ranking by block 204 and filtering by block 206. Theinformation integrated by block 202 can include, e.g., warranty claimsfrom dealers, call records from a call center, telematics from a sensorsystem, and the like. Block 202 can correspond, e.g., to blocks 120 and122 in FIG. 1, block 204 can correspond, e.g., to blocks 124-128 andprocess 112 in FIG. 1, with block 206 corresponding to knowledge base108 interacting with one or more applications 130.

The above ranking-based filtering system aims at help quality analystsfocus on important information. This example assumes that informationcan be categorized according to vehicle parts. Therefore, the rankingsystem identifies important parts, and only the information that isassociated with these parts is transferred to quality analysts. Theranking system can also work, e.g., with a sorting system that listsinformation according to importance scores.

By way of example and not limitation, one ranking mechanism can be basedon the Key Performance Indicator (KPI) for automotive aftermarketservice. The KPIs used can include cost to repair vehicles and customersatisfaction. The former can be quantified as the total cost and thelabor hours to repair a car, while the latter can be quantified as thenumber of failures. Intrinsically, the parts with high repair cost andhigh failure rate should be given more attention. Using the above KPIs,one objective of the ranking mechanism can be to determine a few vehicleparts that dominate repair cost, labor hours and number of failures.

Ranking Techniques

Assuming that all three metrics, namely, repair cost (C), labor hours(L), and number of failures (N), have equal importance to amanufacturer, one can measure the importance of a part (I) using thefollowing formula:

$\begin{matrix}{S_{i} = {\frac{C}{{Max}\left\{ C \right\}} + \frac{L}{{Max}\left\{ L \right\}} + \frac{N}{{Max}\left\{ N \right\}}}} & (2)\end{matrix}$

-   -   Where:    -   C: total repair cost of the part i    -   L: total labor hours for reparing the part i    -   N: number of failures of the part i

The above formula integrates three dimensions of information regardingpart importance according to KPIs. The value of each dimension is thetotal amount observed for the given part until the ranking time. Usingaccumulative history regarding a part, driven by the KPIs, one canmeasure the macro-view of the service performance of each part ratherthan that of each individual vehicle. The above equation is scalablewith the amount of information we learn about each part when usingauxiliary data structures to update the information for each part anddimension incrementally.

The value of a dimension is mapped (normalized) to the range [0, 1.0] sothat the maximum value of each dimension carries a value of 1.0. Thistransformation allows an equal influence to the importance score I of apart from multiple dimensions. When the ranking function should givedifferent weights to different dimensions, the weights can be assignedseparately. Note that one could instead choose to map to the range [0,100.0], or some other convenient range.

The following pseudo-code shows an inventive ranking technique thatupdates importance scores of parts daily. In this technique, K is aparameter to determine the scope of selected parts. The results of theranking algorithm are employed as input to the filtering system in FIG.2 to filter out some or all of the information that does not relate tothe parts determined to be important.

Ranking technique: During day time:  For each warranty claim   Addrepair cost to the total cost of the failed part   Add labor hours tothe total labor hours of the failed part   Increase the total number offailures of the failed part by 1 At the end of a day:  For each part  Calculate importance score of the part using equation 2

FIG. 3 shows some factors affecting the importance of parts. The costfactor addresses concerns about financial issues; the customer factormeasures customer satisfaction, and the safety factor addresses theseverity of a part failure causing unsafe conditions. Each parent factormay combine several child factors, and the knowledge can be providedmanually by human experts or can be learned via analytical techniques.For example, the influence of a part on customer satisfaction can beestimated from the number of car failures caused, or can be extractedform a customer survey. The solid lines link to the factors that areaddressed in the example provided. FIG. 3 is exemplary of one possibleway to rank, namely, with a ranking tree. Each attribute is placed in acategory, one weighs each attribute, and the categories can also beweighted. The bottom row represents leaf nodes; these are the attributesthat get mapped to; each one has a weight. The entries in the middle roware categories. It will be appreciated that multiple levels are possiblebut are omitted for illustrative clarity.

It should be appreciated that an example has been presented within thecontext of automotive parts, but one or more inventive techniques can beadapted to a wide variety of problems.

Given the teachings herein, it will be appreciated that one or morewell-known ranking mechanisms widely applied in information retrieval(IR) can be adapted for ranking-based information processing. Examplesinclude Google® ranking technology, described athttp://www.google.com/technology/, and the technology described in J.Kleinberg, “Authoritative sources in a hyperlinked environment,” Journalof the ACM 46 (1999). When dealing with large data sets, as ininformation retrieval, it may be desirable to rank information entitiesaccording to their importance. Unlike traditional studies on IR, whichdeal with web information, one or more inventive ranking mechanismsherein can focus on knowledge extraction in an enterprise environment.One or more inventive techniques can be configured to deal with diverseheterogeneous information sources such as web information, sensingsystems, and the like (from whence can arise the three types ofheterogeneity discussed above).

If considering applications that need to query enterprise information inreal-time, one or more inventive approaches can be extended usingtechniques for processing queries over stream data. Skilled artisanswill be familiar with the STREAM project at Stanford University, whichaims to develop a general-purpose system for processing continuousqueries over multiple continuous data streams and stored relations. SeeA. Arasu et al., “STREAM: The Stanford Stream Data Manager Demonstrationdescription—short overview of system status and plans,” in Proc. of theACM Intl Conf. on Management of Data (SIGMOD 2003), June 2003. Onesignificant capability of STREAM is handling high-volume and bursty datastreams and complex continuous queries. Borealis (formerly known asAurora) at Brown University has been described as a second-generationSPE (Stream Processing Engine). See Daniel J Abadi, et al., “The Designof the Borealis Stream Processing Engine,” Second Biennial Conference onInnovative Data Systems Research (CIDR 2005), Asilomar, Calif., January2005. When addressing the two challenges of simultaneously optimizingdifferent quality of service (QoS) metrics and optimizing at multiplelevels of granularity of a given system, Abadi and his colleagues pointout the importance of dynamically revising query results and queryspecifications. Similar principles may be of interest with the inventivetechniques herein.

FIG. 4 shows workflow in a flow chart 400 for an exemplary ranking-baseddisplay tool for, e.g., data experts. The left flow can be taken whenapplying built-in ranking or weighting functions based on objectiveenterprise metrics such as KPIs. The framework also permits input basedon domain knowledge. Domain experts can tune the ranking criteria bychoosing some or all dimensions, the parameters, and the function typefor the utility function. It will be appreciated that the depictedtechniques are adaptable and extensible for different solutionrequirements.

Still referring to FIG. 4, the ranking criteria discussed with respectto FIG. 1 can, if desired, be obtained as shown. Step 402 includesobtaining a domain selection. Step 404 includes obtaining a decisionwhether to employ a built-in ranking function. If decision block 404 isaffirmative (left side), step 406 includes obtaining a selection of theranking function from a list, the list being related to the domainselection. If decision block 404 is negative (right side), step 408includes obtaining a selection of dimensions, step 410 includesobtaining a selection of a function type, step 412 includes settingfunction parameters, and step 414 includes generating a non-built-inranking function. One or more of the obtaining the selection ofdimensions, the obtaining the selection of the function type, and thesetting of the function parameters is based at least on the domainselection.

Step 416 can include normalizing event data to obtain normalized eventdata. Step 418 can include ranking the normalized event data to obtainranked normalized event data. Step 420 can include sorting the rankednormalized event data to obtain the integrated information for theprocessing. If desired, instead of or in lieu of using the informationfor processing, one can format the integrated information for display,as at block 422.

It will be appreciated that ranking functions generally apply toparticular domains, and that when a function is to be selected from alist, the list presented will typically depend on the domain. Further,the dimensions, function type and function parameters will normallydepend on the domain, e.g., warranty claims. Data normalization block416 generally normalizes data from actual events, e.g., warranty claims.Such claims may have attributes that can be normalized, for example,ranking on labor costs to permit a meaningful comparison with the numberof parts replaced. Each ranking function type (e.g., a weighted tree)can have parameters. Various ranking criteria are those things deemedimportant. Thus, in a formula, the variables could be normalized eventattributes and the parameters could be the constants.

One or more inventive techniques can deal with source heterogeneity,knowledge heterogeneity and context heterogeneity in integrating andanalyzing information from heterogeneous sources. The example case studyon automotive failure analysis is illustrative of general techniques tobuild and apply ranking knowledge for determining key enterpriseinformation.

A variety of techniques, utilizing dedicated hardware, general purposeprocessors, firmware, software, or a combination of the foregoing may beemployed to implement the present invention. One or more embodiments ofthe invention can be implemented in the form of a computer productincluding a computer usable medium with computer usable program code forperforming the method steps indicated. Furthermore, one or moreembodiments of the invention can be implemented in the form of anapparatus including a memory and at least one processor that is coupledto the memory and operative to perform exemplary method steps.

At present, it is believed that the preferred implementation will makesubstantial use of software running on a general purpose computer orworkstation. With reference to FIG. 5, such an implementation mightemploy, for example, a processor 502, a memory 504, and an input/outputinterface formed, for example, by a display 506 and a keyboard 508. Theterm “processor” as used herein is intended to include any processingdevice, such as, for example, one that includes a CPU (centralprocessing unit) and/or other forms of processing circuitry. Further,the term “processor” may refer to more than one individual processor.The term “memory” is intended to include memory associated with aprocessor or CPU, such as, for example, RAM (random access memory), ROM(read only memory), a fixed memory device (e.g., hard drive), aremovable memory device (e.g., diskette), a flash memory and the like.In addition, the phrase “input/output interface” as used herein, isintended to include, for example, one or more mechanisms for inputtingdata to the processing unit (e.g., mouse), and one or more mechanismsfor providing results associated with the processing unit (e.g.,printer). The processor 502, memory 504, and input/output interface suchas display 506 and keyboard 508 can be interconnected, for example, viabus 510 as part of a data processing unit 512. Suitableinterconnections, for example via bus 510, can also be provided to anetwork interface 514, such as a network card, which can be provided tointerface with a computer network, and to a media interface 516, such asa diskette or CD-ROM drive, which can be provided to interface withmedia 518.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in one or more of the associated memory devices (e.g., ROM,fixed or removable memory) and, when ready to be utilized, loaded inpart or in whole (e.g., into RAM) and executed by a CPU. Such softwarecould include, but is not limited to, firmware, resident software,microcode, and the like.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable medium(e.g., media 518) providing program code for use by or in connectionwith a computer or any instruction execution system. For the purposes ofthis description, a computer usable or computer readable medium can beany apparatus for use by or in connection with the instruction executionsystem, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid-state memory (e.g. memory 504), magnetic tape, aremovable computer diskette (e.g. media 518), a random access memory(RAM), a read-only memory (ROM), a rigid magnetic disk and an opticaldisk. Current examples of optical disks include compact disk-read onlymemory (CD-ROM), compact disk-read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor 502 coupled directly orindirectly to memory elements 504 through a system bus 510. The memoryelements can include local memory employed during actual execution ofthe program code, bulk storage, and cache memories which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringexecution.

Input/output or I/O devices (including but not limited to keyboards 508,displays 506, pointing devices, and the like) can be coupled to thesystem either directly (such as via bus 510) or through intervening I/Ocontrollers (omitted for clarity).

Network adapters such as network interface 514 may also be coupled tothe system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Modems, cable modem andEthernet cards are just a few of the currently available types ofnetwork adapters.

In any case, it should be understood that the components illustratedherein may be implemented in various forms of hardware, software, orcombinations thereof, e.g., application specific integrated circuit(s)(ASICS), functional circuitry, one or more appropriately programmedgeneral purpose digital computers with associated memory, and the like.Given the teachings of the invention provided herein, one of ordinaryskill in the related art will be able to contemplate otherimplementations of the components of the invention.

Although illustrative embodiments of the present invention have beendescribed herein with reference to the accompanying drawings, it is tobe understood that the invention is not limited to those preciseembodiments, and that various other changes and modifications may bemade by one skilled in the art without departing from the scope orspirit of the invention.

1. A computer-implemented method for ranking-based informationprocessing, said method comprising the steps of: integrating informationfrom at least one source, to obtain integrated information; mapping saidintegrated information to at least one application; ranking saidintegrated information based on said mapping and on ranking criteriafrom a knowledge base; processing said integrated information, based onsaid ranking, to obtain processed integrated information; extractingprocess knowledge from a plurality of process knowledge sources;obtaining ranking criteria for said knowledge base, based at least onsaid process knowledge; and formatting said integrated information fordisplay; wherein: in said integrating step, said information is from aplurality of heterogeneous sources, and wherein said integrating stepcomprises integrating said information from said plurality ofheterogeneous sources, to obtain said integrated information, saidintegrated information comprising warranty claims for automotive repairwith an identification of corresponding failed parts; said obtaining ofsaid ranking criteria comprises: obtaining a domain selection, saiddomain selection comprising automotive quality control; obtaining adecision whether to employ a built-in ranking function; and responsiveto said decision being affirmative, obtaining a selection of saidranking function from a list, said list being related to said domainselection; said ranking step comprises: normalizing event data to obtainnormalized event data; ranking said normalized event data to obtainranked normalized event data, said ranking of said normalized event datain turn comprising: during a given day, for each of a plurality of saidwarranty claims, increasing total cost, labor hours, and total number offailures for an appropriate one of said corresponding failed parts; andranking importance of said corresponding failed parts based on theequation${Si} = {\frac{C}{{Max}\left\{ C \right\}} + \frac{L}{{Max}\left\{ L \right\}} + \frac{N}{{Max}\left\{ N \right\}}}$where: C is total repair cost of part i; L is total labor hours forrepairing part i, and N is total number of failures of part i; saidranking further comprises updating a stored representation in a computermemory, said stored representation representing failure of said failedparts, said failed parts being tangible physical items; and sorting saidranked normalized event data to obtain said integrated information forsaid processing.